Predicting response to immunomodulatory drugs (imids) in multiple myeloma patients

ABSTRACT

The present disclosure relates to methods and kits for classifying an individual afflicted with multiple myeloma based on the likelihood of response to immunomodulatory drugs (IMiDs), such as thalidomide and lenalidomide. The disclosure further relates to methods of treating an individual afflicted with multiple myeloma with an IMiD and with methods for determining a therapy regime based on the likelihood of response to an IMiD as a result of genetic characteristic of the patient.

FIELD OF THE INVENTION

The present disclosure relates to methods and kits for classifying an individual afflicted with multiple myeloma based on the likelihood of response to immunomodulatory drugs (IMiDs), such as thalidomide and lenalidomide. The disclosure further relates to methods of treating an individual afflicted with Multiple Myeloma with an IMiD and with methods for determining a therapy regime based on the likelihood of response to an IMiD.

BACKGROUND OF THE INVENTION

Multiple Myeloma (MM), also known as plasma cell leukemia or Kahler's disease, is a cancer of plasma cells, a type of white blood cell normally responsible for producing antibodies. In MM, collections of abnormal plasma cells accumulate in the bone marrow, where they interfere with the production of normal blood cells. Most cases of MM also feature the production of a paraprotein, an abnormal antibody which can cause kidney problems. Bone lesions and hypercalcemia (high blood calcium levels) are also often encountered.

MM is a heterogeneous disease in terms of genetic background, survival and treatment response, for which several ‘novel agents’ are in development.^(1,2) Despite the fact that the disease remains still incurable at this moment in time, these advances have resulted in a clear improvement in the outcome of MM patients.³ For example, the proteasome inhibitor Bortezomib was shown to provide significantly prolonged Progression Free Survival (PFS), and Overall Survival (OS), when compared against non-Bortezomib containing regimes such as VAD.^(4,5) However, the survival improvements are typically assessed at the group level, disregarding the inhomogeneous nature of the disease. It therefore does not show whether all patients have a small survival benefit, or whether a subgroup of patients has a large benefit. In addition, the high costs and potentially dangerous side effects from these treatments argue for limiting treatment with a drug to only those patients expected to benefit from treatment. The numerous (expensive) drugs on the market and in development for MM, the inhomogeneity of the disease, and the severity of the side effects signify a strong need for predictive markers for MM treatment that would allow personalized treatment to further increase the outcome and quality of life for the individual MM patient

SUMMARY OF THE INVENTION

In one embodiment, methods are provided for classifying an individual with multiple myeloma based on the likelihood of response to treatment with an immunomodulatory drug (IMiD), the method comprising:

a) determining in a sample from said individual the level of expression of at least one marker selected from Table 11;

b) determining in a sample from said individual the presence of the t(4;14) translocation;

c) determining in a sample from said individual the level of expression of at least one marker in Table 3; and/or

d) determining in a sample from said individual the presence of the t(11;14) translocation;

wherein the individual is classified based on at least one of steps a), b), c), and d).

Preferably, the individual is classified as

i) a likely responder to thalidomide or an analog thereof which is not substituted with NH₂ or CH₃ at the C4 of the phthaloyl ring and a likely responder to a thalidomide analog which is substituted with NH₂ or CH₃ at the C4 of the phthaloyl ring,

ii) a likely responder to a thalidomide analog which is substituted with NH₂ or CH₃ at the C4 of the phthaloyl ring and a likely non-responder to thalidomide or an analog thereof which is not substituted with NH₂ or CH₃ at the C4 of the phthaloyl ring, or

iii) a likely non-responder to a thalidomide analog which is substituted with NH₂ or CH₃ at the C4 of the phthaloyl ring and a likely responder to thalidomide or an analog thereof which is not substituted with NH₂ or CH₃ at the C4 of the phthaloyl ring.

Preferably, the method comprises

a) determining in a sample from said individual the level of expression of at least one markers selected from Table 11 and/or b) determining in a sample from said individual the presence of the t(4;14) translocation; and

c) determining in a sample from said individual the level of expression of at least one marker in Table 3 and/or d) determining in a sample from said individual the presence of the t(11;14) translocation;

wherein the individual is classified based on steps a) and/or b) and on steps c) and/or d).

Preferably, the methods disclosed herein comprise gene expression profiling.

In one embodiment, methods are provided for treating an individual for multiple myeloma comprising

a) determining in a sample from said individual the level of expression of at least one marker selected from Table 11;

b) determining in a sample from said individual the presence of the t(4;14) translocation;

c) determining in a sample from said individual the level of expression of at least one marker in Table 3; and/or

d) determining in a sample from said individual the presence of the t(11;14) translocation;

determining based on steps a), b), c), and/or d) a treatment of the individual, and treating said individual accordingly.

In one embodiment, methods are provided for treating an individual for multiple myeloma comprising administering to an individual in need thereof thalidomide or an analog thereof which is not substituted with NH₂ or CH₃ at the C4 of the phthaloyl ring, wherein said individual is predicted to likely respond to treatment, said prediction being based on the level of expression of at least one marker selected from Table 11, the presence of the t(4;14) translocation, the level of expression of at least one marker selected from Table 3 and/or the presence of the t(11;14) translocation.

In one embodiment, methods are provided for treating an individual for multiple myeloma comprising administering to an individual in need thereof a analog substituted with NH₂ or CH₃ at the C4 of the phthaloyl ring, wherein said individual is predicted to likely respond to treatment, said prediction being based on the level of expression of at least one marker selected from Table 11, the presence of the t(4;14) translocation, the level of expression of at least one marker selected from Table 3 and/or the presence of the t(11;14) translocation.

In one embodiment, thalidomide or an analog thereof which is not substituted with NH₂ or CH₃ at the C4 of the phthaloyl ring is provided for use in the treatment of multiple myeloma in an individual likely to respond to thalidomide treatment, wherein the likelihood of response to thalidomide or the analog thereof is determined by

a) determining in a sample from said individual the level of expression of at least one marker selected from Table 11;

b) determining in a sample from said individual the presence of the t(4;14) translocation;

c) determining in a sample from said individual the level of expression of at least one marker in Table 3; and/or

d) determining in a sample from said individual the presence of the t(11;14) translocation. Preferably, the likelihood of response to thalidomide or the analog thereof is determined by a) determining in a sample from said individual the level of expression of at least one marker selected from Table 11 and/or b) determining in a sample from said individual the presence of the t(4;14) translocation.

Preferably, step a) comprises determining in a sample from said individual the level of expression of at least two markers, wherein at least one marker is selected from Table 11 and at least one marker is selected from Table 11 or Table 12. More preferably, step a) comprises determining the level of expression of the markers from Table 1, the markers from Table 2, and/or the markers from Table 4.

In one embodiment, thalidomide analog which is substituted with NH₂ or CH₃ at the C4 of the phthaloyl ring for use in the treatment of multiple myeloma in an individual likely to respond to the thalidomide analog treatment, and

wherein the likelihood of response to thalidomide analog is determined by

a) determining in a sample from said individual the level of expression of at least one marker selected from Table 11;

b) determining in a sample from said individual the presence of the t(4;14) translocation;

c) determining in a sample from said individual the level of expression of at least one marker in Table 3; and/or

d) determining in a sample from said individual the presence of the t(11;14) translocation. Preferably, the likelihood of response to the thalidomide analog is determined by determining in a sample from said individual the level of expression of at least one marker in Table 3 and/or determining in a sample from said individual the presence of the t(11;14) translocation.

Preferably, the level of marker expression is determined by detection of RNA.

Preferably, the thalidomide analog which is substituted with NH₂ or CH₃ at the C4 of the phthaloyl ring is lenalidomide or pomalidomide.

Preferably, the sample comprises plasma cells.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1: Kaplan Meier curves showing that the SKY92 is significantly prognostic in the H87 dataset for Progression Free Survival (PFS, left), and Overall Survival (OS, right). Blue: SKY92 High Risk; Red: SKY92 Standard Risk.

FIG. 2: Kaplan Meier curves showing the SKY92 High Risk/Standard Risk split into Treatment arms MPT-T and MPR-R. Data from the H87 cohort and for Overall Survival.

FIG. 3: Kaplan Meier curves showing the Virtual t(4;14), MS Cluster, and iFISH t(4;14) positive and negative groups split into Treatment arms MPT-T and MPR-R. Hazard Ratios were calculated within positive patients between treatment arms, and within negative patients between treatment arms. Data from the H87 cohort and for Overall Survival.

FIG. 4: Kaplan Meier curves showing the Virtual t(11;14), and iFISH t(11;14) positive and negative groups split into Treatment arms MPT-T and MPR-R. Hazard Ratios were calculated within positive patients between treatment arms, and within negative patients between treatment arms. Data from the H87 cohort and for Overall Survival.

FIG. 5: Scatterplots showing the Hazard Ratio (TC4-/TC4sub) in the group identified as positive. Hazard Ratios above 1 indicate a better Overall Survival for the MPR-R treatment when compared against MPT-T. Conversely, a Hazard Ratio of smaller than 1 indicates that the MPT-T treatment has a better Overall Survival when compared against MPR-R. Hazard Ratios larger than 15 were set to 15.

DETAILED DESCRIPTION OF THE DISCLOSED EMBODIMENTS

Immunomodulatory drugs (IMiDs), such as thalidomide and lenalidomide, may be used in the treatment of MM. It is believed that IMiDs exert their effect, at least in part, by enhancing CD4+ and CD8+ T cell costimulation. Cereblon (CRBN), a Cullin 4 ring E3 ligase complex, has been shown to be a target of IMiDs and low CRBN levels were found to correlate with poor response (or resistance) to IMiDs.

It has been suggested that biomarkers may predict the response of an MM patient to treatment with IMiDs (see, e.g., WO2012125405 and WO2011020839). Surprisingly, the present disclosure demonstrates that it is possible to distinguish the likelihood of response between different IMiDs for particular patient subsets defined by their genetic characteristics. The present disclosure demonstrates that thalidomide and compounds which are structurally related to thalidomide can be categorized in two separate groups of compounds based on the ability to predict responsiveness in these two groups. A first group comprises thalidomide and analogs thereof which are not substituted with NH₂ or CH₃ at the C4 of the phthaloyl ring, herein referred to collectively as “TC4-compounds”. A second group comprises thalidomide analogs which are substituted with NH₂ or CH₃ at the C4 of the phthaloyl ring, herein referred to as “TC4sub compounds”.

In accordance with the methods and kits described herein, a patient may be classified as likely responding (similarly) to a TC4-compound and a TC4sub compound, as likely responding better to a TC4-compound than a TC4sub compound, or as likely responding better to a TC4sub compound than a TC4-compound.

Accordingly, one object of the disclosure is to provide methods and kits that distinguish the response of a patient to a TC4-compound versus the response to a TC4sub compound. Such methods and kits are not only useful for predicting response to an IMiD, but also provide an indication as to which IMiD is likely to be more effective for a particular patient. Accordingly, the methods and kits described herein are also useful in determining a treatment regime.

IMiDs include thalidomide as well as thalidomide analogs. Thalidomide (2-(2,6-dioxopiperidin-3-yl)-1H-isoindole-1,3(2H)-dione) is composed of a glutarimide ring and a phthaloyl ring and has the following chemical structure:

As used herein, a thalidomide analog refers to a compound having the backbone structure of thalidomide (a glutarimide ring and a phthaloyl ring). Such compounds are described, e.g. in US2015/0164877. The thalidomide analogs described herein may include any modification of the thalidomide backbone structure. In preferred embodiments, the thalidomide analog binds to CRBN.

TC4-compounds, as used herein, include thalidomide (which is not substituted at the C4 of the phthaloyl ring) and thalidomide analogs which are not substituted with NH₂ or CH₃ at the C4 of the phthaloyl ring. These analogs include compounds which are not substituted at the C4 of the phthaloyl ring and compounds that contain substitutions such (CH₃)₂, herein referred to collectively as “TC4-compounds”. A preferred TC4-compound of the disclosure is thalidomide.

Preferred TC4sub compounds include lenalidomide and pomalidomide. More preferably the derivative is lenalidomide. Lenalidomide, also known as 3-(4-amino-1-oxo-1,3-dihydro-isoindol-2-yl)-piperidine-2,6-dione (having the tradename Revlimid™) has the following chemical structure:

Pomalidomide, also known as 4-Amino-2-(2,6-dioxopiperidin-3-yl)isoindole-1,3-dione (having the tradenames Imnovid™ and Pomalyst™) has the following chemical structure:

In preferred embodiments, the TC4sub compound binds one or more IKAROS transcription factors (e.g., IKZF1 and IKZF3).

While TC4-compounds and TC4sub compounds are both useful in the treatment of MM, these compounds differ in their biological activity, in particular in their ability to promote ubiquitination of the IKAROS family transcription factors by CRBN. As recently described in Fischer et al. (Nature. 2014 Jul. 16 DOI: 10.1038/nature13527), thalidomide, lenalidomide, and pomalidomide all bind similarly to CRBN. However, lenalidomide, pomalidomide, and 2-(2,6-dioxopiperidin-3-yl)-4-methylisoindoline-1,3-dione (a thalidomide analog having a CH₃ substitution at the C4 of the phthaloyl ring) are more efficient at targeting IKAROS transcription factors for degradation by CRBN than thalidomide. While not wishing to be bound by theory, it is likely that the differences in patient response to IMiD treatment described herein are related to the differential targeting of IKAROS transcription factors.

One aspect of the disclosure provides methods for classifying an individual with MM based on the likelihood of response to treatment with an immunomodulatory drug (IMiD). The individual is classified as a likely responder to a TC4-compound and a likely responder to a TC4sub compound, as a likely non-responder to a TC4sub compound and a likely responder to a TC4-compound, or as a likely responder to a TC4sub compound and a likely non-responder to a TC4-compound.

Said methods comprise determining in a sample from said individual:

1. the level of expression of at least one marker selected from Table 1, Table 2, Table 4, Table 11, and Table 12;

2. the presence of the t(4;14) translocation;

3. the level of expression of at least one marker in Table 3;

4. the presence of the t(11;14) translocation; and/or wherein the individual is classified based on at least one of the steps above.

Preferably the method comprises steps 1, 2, 3, and 4. Preferably the method comprises steps 1, 2, and/or 3. Preferably the method comprises steps 1 or 2. Preferably the method comprises steps (1 or 2) and (3 or 4). Preferably the method comprises steps (1 or 2) and 4. Preferably the method comprises steps 3 or 4. Preferably the method comprises step 1. Preferably the method comprises step 2. Preferably the method comprises step 3. Preferably the method comprises step 4. Preferably the method comprises step 5. Preferably the method comprises steps 1 and 3. Preferably the method comprises steps 2 and 3. Preferably the method comprises steps 1 and 4. Preferably the method comprises steps 2 and 4.

As described in the examples, the disclosure demonstrates that the level of expression of at least one marker selected from Table 1, Table 2, Table 4, Table 11, and Table 12 (step 1) can be used to classify whether the individual is a likely responder to a TC4sub compound and a likely non-responder to a TC4-compound or that the individual is a likely responder to a TC4sub compound and a likely responder to a TC4-compound. The Tables list Affymetrix probesets and their corresponding “markers” (genes).

In preferred embodiments, the level of expression of at least two markers selected from Table 1, Table 2, Table 4, Table 11, and Table 12 is determined. In some embodiments, the level of expression of at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least 10, at least 20, or at least 30 markers selected from Table 1-4, Table 11, and Table 12 is determined. In preferred embodiments, the level of expression of at least two markers selected from Table 1 is determined. In preferred embodiments, the level of expression of at least two markers selected from Table 2 is determined. In preferred embodiments, the level of expression of at least two markers selected from Table 4 is determined. In preferred embodiments, the level of expression of at least two markers selected from Table 12 is determined.

In preferred embodiments, the level of expression of all markers from Table 1 is determined. In preferred embodiments, the level of expression of all markers from Table 2 is determined. In preferred embodiments, the level of expression of all markers from Table 4 is determined. In preferred embodiments, the level of expression of all markers from Table 12 is determined.

In more preferred embodiments, the level of expression of at least one marker from Table 11 is determined in the methods. As described herein, Table 11 depicts markers which can each, independently, identify patients that have a higher likelihood of responding to a TC4sub compound than to a TC4-compound.

In some embodiments, the level of expression of at least two markers is determined, wherein at least one marker is selected from Table 11 and at least one marker is selected from Table 11 or Table 12. In some embodiments, the level of expression of at least three markers is determined, wherein at least one marker is selected from Table 11 and at least two markers are selected from Table 11 or Table 12. In some embodiments, the level of expression of at least four markers is determined, wherein at least one marker is selected from Table 11 and at least three markers are selected from Table 11 or Table 12. In some embodiments, the level of expression of at least five markers is determined, wherein at least one marker is selected from Table 11 and at least four markers are selected from Table 11 or Table 12. In some embodiments, the level of expression of at least ten markers is determined, wherein at least one marker is selected from Table 11 and at least nine markers are selected from Table 11 or Table 12.

An individual is classified into one of two groups based on the level of marker expression and whether the level is altered or “differentially expressed” as compared to a reference. Determining the level of expression includes the expression of nucleic acid, preferably mRNA, or the expression of protein. In some embodiments, nucleic acid or protein is purified from the sample and the marker is measured by nucleic acid or protein expression analysis. Preferably, the sample comprises plasma cells. Although a preferred source of plasma cells is a bone marrow sample, other plasma cell containing samples, such as, e.g., blood, may also be used.

Table 1, Table 2, Table 4, Table 11, and Table 12 list Affymetrix DNA probes corresponding to particular genes, i.e., “markers”, as used herein. Marker expression can be measured at the level of nucleic acid or protein.

It is clear to a skilled person, that the term “the level of expression of at least one marker selected from Table 1, Table 2, Table 4, Table 11, and Table 12” refers to the level of nucleic acid corresponding to the probes listed in the table or the corresponding genes they refer to. It is well within the purview of a skilled person to develop additional probes that detect the markers referred to in the tables. The level of nucleic acid expression may be determined by any method known in the art including RT-PCR, quantitative PCR, Northern blotting, gene sequencing, in particular RNA sequencing, and gene expression profiling techniques. Preferably, the level of nucleic acid using a microarray.

Preferably, the nucleic acid is RNA, such as mRNA or pre-mRNA. As is understood by a skilled person, the level of RNA expression determined may be detected directly or it may be determined indirectly, for example, by first generating cDNA and/or by amplifying the RNA/cDNA. The level of expression need not be an absolute value but rather a normalized expression value or a relative value.

It is clear to a skilled person, that in some embodiments the term “the level of expression of at least one marker selected from Table 1, Table 2, Table 4, Table 11, and Table 12” refers to the level of protein corresponding to the probes or the genes they refer to. The level of expression can be determined by any method known in the art including ELISAs, immunocytochemistry, flow cytometry, Western blotting, proteomic, and mass spectrometry.

Preferably, the level of expression refers to a “normalized” level of expression. Normalization is particularly useful when expression is determined based on microarray data. Normalization allows for correction for variation within microarrays and across samples so that data from different chips can be simultaneously analyzed. The robust multi-array analysis (RMA) algorithm may be used to pre-process probe set data into gene expression levels for all samples. (Irizarry R A, et al., Biostatistics (2003) and Irizarry R A, et al., Nucleic Acids Res. (2003)). In addition, Affymetrix's default preprocessing algorithm (MAS 5.0), may also be employed. Additional methods of normalizing expression data are described in US20060136145.

As used herein, the term “differentially-expressed” means that the measured expression level in a subject differs significantly from a reference. The reference may be a single value or a numerical range. It is within the purview of a skilled person to determine the appropriate reference value. In some embodiments, the reference value is a predetermined value. In some embodiments, the reference value is the average of the expression value in a particular patient class. For example, the reference value may be the average of the expression value in the class of patients that are predicted to respond to both a TC4-compound and TC4sub compounds). A reference value may also be in the form of or derived from an equation, see, e.g., equations 1 and 2 herein. In preferred embodiments, the reference may be an m₀ or m₁ value as described herein. The reference may also be several values, e.g., the comparison between an m₀ or m₁ value as described herein. It is within the purview of one skilled in the art to determine whether the expression level in the patient differs “significantly” from a reference.

In an exemplary embodiment, the reference value is determined from the HOVON-87/NMSG-18 study, in which response to thalidomide treatment was compared to lenalidomide treatment in MM patients. It is clear to a skilled person that data from similar studies may also be used.

The strength of the correlation between the expression level of a differentially-expressed gene and a specific patient response class may be determined by a statistical test of significance. For example, a chi square test may be used to assign a chi square value to each differentially-expressed marker, indicating the strength of the correlation of the expression of that marker to a specific patient response class. Similarly, the T-statistics metric and the Wilkins' metric both provide a value or score indicative of the strength of the correlation between the expression of the marker and its specific patient response class. In addition, SAM or PAM analysis tools may be used to determine the strength of correlations.

In some embodiments, the subject expression profile (or rather, the expression level of one or more markers) is compared to the reference expression profile to determine whether the subject expression profile is sufficiently similar to the reference profile. Alternatively, the subject expression profile is compared to a plurality of reference expression profiles to select the reference expression profile that is most similar to the subject expression profile. Any method known in the art for comparing two or more data sets to detect similarity between them may be used to compare the subject expression profile to the reference expression profiles.

In machine learning and statistics, classification refers to identifying to which set of categories a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known. An algorithm that implements classification, especially in a concrete implementation, is known as a classifier. Many classifiers are known in the art, with linear or non-linear classifier boundaries, such as but not limited to: ClaNC, nearest mean classifier, weighted voting method, simple Bayes classifier, linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), Support Vector Machines (SVM), or the k-nearest neighbor (k-nn) classifier.

In a preferred embodiment, a linear classifier is used in the methods described herein. The ClaNC classifier (Classification to Nearest Centroids) is a preferred linear classifier and is described in detail in the examples. Briefly, for a single MM patient referred to as x, a distance d to each of the two centroids is calculated. Centroids are referred to with 0 and 1 subscripts. The employed distance is the normalized Euclidean distance measure, resulting in a d0 and a d1, formulated as:

$\begin{matrix} {{{d_{0}(x)} = \sqrt{\sum\limits_{i = 1}^{N}\; \frac{\left( {x_{i} - m_{0,i}} \right)^{2}}{s_{0,i}^{2}}}}{and}} & {{Equation}\mspace{14mu} 1} \\ {{d_{1}(x)} = \sqrt{\sum\limits_{i = 1}^{N}\; \frac{\left( {x_{i} - m_{1,i}} \right)^{2}}{s_{1,i}^{2}}}} & {{Equation}\mspace{14mu} 2} \end{matrix}$

wherein x_(i) represents the expression level of a particular gene i of the MM patient x, N is the total number of genes or probesets used in the particular classifier, m_(i) is the mean of the centroid for gene or probeset i, and s_(i) the standard deviation of the centroid for gene/probeset i. The MM patient is then assigned to the group with the smallest distance d (i.e. the closest centroid).

Tables 2 and 4 provide exemplary values for m₀, m₁, s₀ and s₁ which may be used as a guideline. It is clear to a skilled person that the values listed in the tables may be rounded off to one or two significant digits. The examples also describe how the expression level of a single marker or a collection of markers classify patient response when using the ClaNC classifier. In preferred embodiments, the ClaNC classifier is used in the methods described herein for markers listed in Tables 2 and Table 4.

The weighted voting method is also a preferred linear classifier and is described in detail in the examples. Briefly, for each marker used, a vote for one or the other class (e.g., responder to a TC4-compound and derivative TC4sub compound or a responder to TC4sub compound and non-responder to a TC4-compound) is determined based on expression level. Each vote is then weighted in accordance with the weighted voting scheme (for example the beta values listed in Table 1), and the weighted votes are summed to determine the winning class for the sample.

In an exemplary embodiment, the following formula may be used to classify an individual:

$\begin{matrix} {{{SKY92}(x)} = {\sum\limits_{i = 1}^{92}\; {\beta_{i}x_{i}}}} & {{Equation}\mspace{14mu} 3} \end{matrix}$

where βi represents the weight factor of gene i, and xi represents the expression level of gene i in a patient, x. The beta values are listed in Table 1. However, it is clear to a skilled person that other beta values (i.e. “weights”) may be used. A score above the threshold classifies a patient as a responder to a TC4sub compound and non-responder to a TC4-compound. A score at or below the threshold classifies a patient as a responder to both a TC4sub compound and a TC4-compound.

Table 1 provides exemplary beta values (i.e. “weights”), which may be used as a guideline. However, it is clear to a skilled person that other beta values may be used. In preferred embodiments, the threshold is determined such that the top 15-25%, preferably the top 21.7%, scores of an unselected MM patient cohort fall above the threshold. In the exemplary embodiment disclosed in Example 1, this results in a threshold of 0.7774. However, it is clear to a skilled person that other threshold values may be used. It is also clear to a skilled person that the threshold may be rounded off to one or two significant digits.

In preferred embodiments, the weighted voting method is used in the methods described herein for markers listed in Table 1.

In some embodiments, a subset of the 92 markers of Table 1 is used. In such cases, it is possible to keep the weights of the subset as provided in Table 1 and retrain a new threshold as the top 21.7% of the SKY92 scores. Table 13 provides exemplary threshold values for when only one probeset is used in the methods.

Alternatively, the existing threshold is used and the weight of the discarded markers is redistributed to the remaining genes based on the covariance structure in the training set (HOVON65/GMMG-HD4). Such modifications are within the purview of one of skill in the art. The examples also describe how the expression level of a single marker or a collection of markers classify patient response when using the weighted voting classifier.

In preferred embodiments, the method comprises

a) providing a gene chip comprising probes for the detection of one or more markers selected from Table 1 as described above, in particular including a probe for the detection of a marker that is in both Table 1 and Table 11,

b) contacting the gene chip with nucleic acid obtained directly (e.g. RNA from the sample) or indirectly (e.g., RNA or DNA that has been processed/amplified from the sample) from a sample from a patient,

c) determining the expression levels of the marker(s) in the sample,

d) normalizing the expression levels using mean/variance normalization in order to obtain the normalized expression value

e) multiply the normalized expression value from markers from Table 1 (and those also found in Table 11 or 12) with a beta value (i.e. weight vote, preferably the beta value in Table 1) to obtain the calculated value for an individual probe, f) determine a score by summation of the calculated values of the individual probe(s),

wherein a score above a predetermined threshold (reference value) indicates that the patient is to be classified as a likely responder to derivative TC4sub compound and a likely non-responder to a TC4-compound and a score at or below the predetermined threshold indicates that the patient is to be classified as a likely responder to both a TC4-compound and a TC4sub compound.

In preferred embodiments, the method comprises

a) providing a gene chip comprising probes for the detection of one or more markers selected from Table 2 as described above, in particular including a probe for the detection of a marker that is in both Table 2 and Table 11, as described above,

b) contacting the gene chip with nucleic acid obtained directly (e.g. RNA from the sample) or indirectly (e.g., RNA or DNA that has been processed/amplified from the sample) from a sample from a patient,

c) determining the expression level of the marker(s) in the sample,

d) normalizing the expression level using mean/variance normalization in order to obtain a normalized expression value,

e) solving equations 1 and 2 to obtain d₀ and d₁ values using the normalized expression value from the marker(s) and the m₀, m_(i), s₀, and s_(i) values from Table 2,

wherein when d₀<d₁, the individual is classified as a likely responder to both a TC4-compound and a TC4sub compound and when d₀ is greater than or equal to d₁, the individual is classified as a likely responder to TC4sub compound and a likely non-responder to a TC4-compound.

In preferred embodiments, the method comprises

a) providing a gene chip comprising probes for the detection of one or more marker selected from Table 4 as described above, in particular including a probe for the detection of a marker that is in both Table 4 and Table 11,

b) contacting the gene chip with nucleic acid obtained directly (e.g. RNA from the sample) or indirectly (e.g., RNA or DNA that has been processed/amplified from the sample) from a sample from a patient,

c) determining the expression level of the marker(s) in the sample,

d) normalizing the expression level using mean/variance normalization in order to obtain a normalized expression value,

e) solving equations 1 and 2 to obtain do and d₁ values using the normalized expression value from the marker(s) and the m₀, m_(i), s₀, and s_(i) values from Table 4, wherein when d₀<d₁, the individual is classified as a likely responder to both a TC4-compound and a TC4sub compound and when d₀ is greater than or equal to d₁, the individual is classified as a likely responder to TC4sub compound and a likely non-responder to a TC4-compound.

As described in the examples and depicted in FIG. 3, the disclosure demonstrates that the presence of the t(4;14) translocation (step 2) indicates that the individual is a likely responder to a TC4sub compound and a likely non-responder to a TC4-compound. Conversely, the absence of the t(4;14) translocation indicates that the individual is a likely responder to a TC4sub compound and a likely responder to a TC4-compound.

The presence of the t(4;14) translocation can be determined by any means known to a skilled person. As is well known to a skilled person, translocations may be detected by, for example, multiplex ligation dependent probe amplification, by G-banding or R-banding techniques, by comparative genomic hybridization (CGH) such as array-CGH or equivalent DNA copy number aberration (CNA) techniques. In an exemplary embodiment, fluorescence in situ hybridization (FISH) may be used to detect a translocation. Malgeri et al. (Cancer research. 2000; 60 (15): 4058-4061) describes the detection of the t(4;14) translocation using both iFISH and RT-PCR. As it is known that translocation t(4;14) involves FGFR3 and MMSET, the use of markers for FGFR3 and/or MMSET are preferred.

In some embodiments, the presence of the t(4;14) translocation can be determined using a gene expression based profile. Table 2 provides an exemplary list of probe sets which can be used to determine the presence of the t(4;14) translocation.

As described in the examples, the disclosure demonstrates that the level of expression of at least one marker selected from Table 3 (step 3) can be used to classify whether the individual is a likely non-responder to a TC4sub compound and a likely responder to a TC4-compound or that the individual is a likely responder to a TC4sub compound and a likely responder to a TC4-compound.

In preferred embodiments, the level of expression of at least two markers selected from Table 3 or at least three markers selected from Table 3 is determined.

In preferred embodiments, the method comprises

a) providing a gene chip comprising probes for the detection of one or more marker selected from Table 3 as described above,

b) contacting the gene chip with nucleic acid obtained directly (e.g. RNA from the sample) or indirectly (e.g., RNA or DNA that has been processed/amplified from the sample) from a sample from a patient,

c) determining the expression level of the marker(s) in the sample,

d) normalizing the expression level using mean/variance normalization in order to obtain a normalized expression value,

e) solving equations 1 and 2 to obtain do and d₁ values using the normalized expression value from the marker(s) and the m₀, m_(i), s₀, and s_(i) values from Table 3,

wherein when d₀<d₁, the individual is classified as a likely responder to both a TC4-compound and a TC4sub compound and when d₀ is greater than or equal to d₁, the individual is classified as a likely responder to a TC4-compound and a likely non-responder to a TC4sub compound.

As discussed previously herein, an individual is classified into one of two groups based on the level of marker expression and whether the level is altered or “differentially expressed” as compared to a reference value. In an exemplary embodiment, the reference value is determined from the HOVON-87/NMSG-18 study.

In preferred embodiments, an ClaNC classifier as described herein is used in the methods described herein for the markers listed in Table 3. Table 3 provides exemplary values for m₀, m_(i), s₀, and s_(i) values which may be used as a guideline. However, it is clear to a skilled person that that values that above or below these numbers will still yield satisfactory results. The examples also describe how the expression level of a single marker or a collection of markers classify patient response when using the ClaNC classifier.

As described in the examples and depicted in FIG. 3, the disclosure demonstrates that the presence of the t(11;14) translocation (step 4) indicates that the individual is a likely non-responder to a TC4sub compound and a likely responder to a TC4-compound. Conversely, the absence of the t(11;14) translocation indicates that the individual is a likely responder to a TC4sub compound and a likely responder to a TC4-compound.

The presence of the t(11;14) translocation can be determined by any means known to a skilled person. As is well known to a skilled person, translocations may be detected by, for example, multiplex ligation dependent probe amplification, by G-banding or R-banding techniques, by comparative genomic hybridization (CGH) such as array-CGH or equivalent DNA copy number aberration (CNA) techniques. In an exemplary embodiment, fluorescence in situ hybridization (FISH) may be used to detect a translocation. As it is known that translocation t(11;14) involves CCND1, the use of markers for CCND1 are preferred (Avet-Loiseau et al. Genes Chromosomes Cancer. 1998 October; 23(2):175-82).

In some embodiments, the presence of the t(11;14) translocation can be determined using a gene expression based profile. Table 3 provides an exemplary list of probe sets which can be used to determine the presence of the t(11;14) translocation.

As used herein, the terms individual, subject, or patient are used interchangeably and include mammals, such as primates and domesticated animals. Preferably said individual is a human.

As used herein, the term “multiple myeloma (MM)” it is meant any type of B-cell malignancy characterized by the accumulation of terminally differentiated B-cells (plasma cells) in the bone marrow, including multiple myeloma cancers which produce light chains of kappa-type and/or light chains of lambda-type; drug resistant multiple myeloma, refractory multiple myeloma or aggressive multiple myeloma, including primary plasma cell leukemia (PCL); and/or optionally including any precursor forms of the disease, including but not limited to benign plasma cell disorders such as MGUS (monoclonal gammopathy of undetermined significance) and/or Waldenstrom's macroglobulinemia (WM, also known as lymphoplasmacytic lymphoma) which may proceed to multiple myeloma; and/or smoldering multiple myeloma (SMM), and/or indolent multiple myeloma, premalignant forms of multiple myeloma which may also proceed to multiple myeloma.

Diagnosis is based on a combination of factors, including the patient's description of symptoms, the doctor's physical examination of the patient, and the results of blood tests and optional x-rays. The diagnosis of multiple myeloma in a subject may occur through any established diagnostic procedure known in the art such as described, e.g., in Rajkumar 2014 (Raikumar Lancet Oncology 2014 Volume 15, Issue 12, e538-e548). Generally, diagnosis of multiple myeloma is made based on either 1) at least 60% of the cells in the bone marrow are plasma cells or 2) the presence of a plasma cell tumor (e.g. identified by biopsy) or least 10% of the cells in the bone marrow are plasma cells; and at least one of the following—high blood calcium level, poor kidney function, low red blood cell counts (anemia), holes in bones from tumor growth found on imaging studies, abnormal area in the bones or bone marrow on an MRI scan, and increase in serum monoclonal Ig.

Smoldering MM refers to early myeloma that is not (yet) causing any (or few) symptoms or problems. Generally, diagnosis of smoldering multiple myeloma is based on one of the following: between 10-60% of the cells in the bone marrow are plasma cells, the presence of high level of monoclonal immunoglobulin (M protein) in the blood, or the presence of high level of light chains in the urine.

In a preferred embodiment, the MM is selected from smoldering MM and symptomatic MM. Preferably, the MM is symptomatic. Symptomatic MM may be defined as, e.g., the presence of a M-protein and/or abnormal free light chain ratio in serum (or urine), and clonal plasma cells in bone marrow or plasmocytoma, and at least 1 myeloma-related dysfunction selected from

-   -   calcium >2.65 mmol/l     -   renal insufficiency (creatinine >177 μmol/l)     -   anemia (Hb<6.2 mmol/l or >1.25 mmol/l below normal limit) or         (Hb<10.0 g/dl or >2.1 g/dl below normal limit)     -   bone disease (lytic lesions or osteopenia).

The methods and kits disclosed herein are useful for predicting the likelihood for responding to treatment. The term “likelihood” refers to the probability of an event. The term likelihood of response refers to probability that, for example, the rate of tumor progress or tumor cell growth will decrease as a result of treatment. As is clear to a skilled person, the term likelihood of response refers to a probability and not that 100% of all patients that are predicted to respond to a treatment may actually respond.

Response to treatment can be measured by any number of endpoints including t ime-to-disease-progression (TTP), growth size of tumor, and clinical prognostic markers (e.g., level of M protein or percentage of plasma cells in bone marrow). In some embodiments, a responder to treatment demonstrates Complete Response (CR), Stringent Complete Response (sCR), Very Good Partial Response (VGPR), or Partial Response (PR), or Stable Disease (SD), increased Time To Progression (TTP), increased Progression Free Survival (PFS) and Overall Survival (OS); as defined by the International Myeloma Working Group (IMWG). In some embodiments, a responder has a lower hazard rate, e.g. a lower chance of having a certain type of event (disease progression/death) with treatment rather than in the absence of treatment. Preferably, an individual is classified as a likely responder to treatment when the Overall Survival (OS) of the patient is predicted to be longer with treatment rather than in the absence of treatment. OS is defined as the time from a given time-point e.g. the moment of diagnosis or randomization until death from any cause, and is measured in the intent-to-treat population.

Preferably, a “likely responder” and a “likely non-responder” are not defined in absolute terms of response, but rather as a comparison between two IMiD treatments. Preferably, an individual classified as a likely responder to a TC4-compound and a likely responder to a TC4sub compound is predicted to respond similarly to both treatments. Preferably, the predicted Hazard Ratio of TC4-compound/TC4sub compound (the ratio of the two hazard rates) would be around 1 in such cases. Preferably, with a p-value of >0.05.

An individual classified as a likely responder to a TC4-compound and a likely non-responder to a TC4sub compound is predicted to respond better to a TC4-compound treatment. Preferably, the predicted Hazard Ratio of TC4-compound/TC4sub compound (the ratio of the two hazard rates) would be HR<1. Preferably, with a p-value of <0.05. It is clear to a skilled person that other endpoints can be used. For example, for these individuals the TTP or PFS or OS is predicted to be longer when treated with a TC4-compound as compared to a TC4sub compound. In another example, for these individuals the hazard rate is predicted to be lower when treated with a TC4-compound as compared to a TC4sub compound.

Conversely, an individual classified as a likely non-responder to a TC4-compound and a likely responder to a TC4sub compound is predicted to respond better to a TC4sub compound treatment. Preferably, the predicted Hazard Ratio of TC4-compound/TC4sub compound (the ratio of the two hazard rates) would be HR>1. Preferably, with a p-value of <0.05. For example, for these individuals the TTP or PFS or OS is predicted to be shorter when treated with a TC4-compound as compared to a TC4sub compound. In another example, for these individuals the hazard rate is predicted to be higher when treated with a TC4-compound as compared to a TC4sub compound.

As is also clear to a skilled person, the likelihood of response can be a dynamic state. Otherwise stated using a hypothetical example, based on the expression levels of the markers described herein, an individual may be classified at time=t, as a responder to a TC4-compound and a responder to a TC4sub compound. However, at time=t+x, the expression levels of the markers described herein may classify the individual as, for example, a responder to a TC4-compound and a non-responder to a TC4sub compound. As is clear to a skilled person, this change in likelihood of response may be due to effects associated with a change of the genetic profile as a result of the progression of disease or the given treatment. This change may also be due to the development of resistance, for example, if the individual is treated with a TC4sub compound after time=t. Accordingly, the methods disclosed herein for classifying an individual with multiple myeloma based on the likelihood of response to treatment with an IMiD, are also useful for determining or monitoring whether an individual is resistant to or acquired resistance to an IMiD. Accordingly, the individual may be classified right after diagnosis, prior to the start of treatment, during treatment, or after the completion of treatment, e.g. to determine the best maintenance treatment for that individual.

One of the advantages of applying the methods disclosed herein to predict response is that it allows for optimizing a treatment regime. Individuals that are predicted to respond to a particular treatment may be subsequently administered such treatment. Conversely, individuals predicted not to respond to a particular treatment may be administered with an alternative treatment. This can result in a decrease in unnecessary treatments.

Accordingly, the disclosure provides a method for treating an individual for multiple myeloma comprising:

1) determining in a sample from said individual the level of expression of at least one marker selected from Table 1, Table 2, Table 4, Table 11, and Table 12 (preferably the number and combinations of markers as disclosed herein);

2) determining in a sample from said individual the presence of the t(4;14) translocation;

3) determining in a sample from said individual the level of expression of at least one marker in Table 3 (preferably the number and combinations of markers as disclosed herein); and/or

4) determining in a sample from said individual the presence of the t(11;14) translocation;

determining based on steps a), b), c), and/or d) a treatment of the individual, and treating said individual accordingly.

Treatments for MM are well-known to a skilled person and include, e.g., radiation, autologous stem cell transplantation, surgery, and drug therapies. Drug therapies include, among others, thalidomide, thalidomide analogs (e.g., lenalidomide, pomalidomide), proteasome inhibitors (e.g., bortezomib), interferon alfa-2b, and steroids like prednisone, Antibody based therapies, HDAC inhibitors, Alkylating agents, pathway inhibitors etc.

Combination treatments are also well-known to a skilled person and include

-   -   doxorubicin/dexamethasone/bortezomib/lenalidomide;     -   vincristine/doxorubicin/dexamethasone     -   Rd, lenalidomide/dexamethasone;     -   MPV, melphalan/prednisone/bortezomib;     -   VRD, Bortezomib, Lenalidomide, Dexamethasone     -   KRd, Carfilzomib, Lenalidomide, low dose Dexamethasone     -   MPT-T, Melphalan, Prednisone, Thalidomide, and Thalidomide         maintenance     -   MPR-R, Melphalan, Prednisone, Lenalidomide, and Lenalidomide         maintenance

In some embodiments, the individual is treated with a TC4-compound. Preferably, the individual is treated with induction therapy with melphalan, prednisone and a TC4-compound, followed by a TC4-compound maintenance. In some embodiments, the individual is treated with a TC4sub compound. Preferably, the individual is treated with induction therapy with melphalan, prednisone and a TC4sub compound, followed by TC4sub compound maintenance.

Preferably the treatment method comprises steps 1, 2, and/or 4. Preferably the method comprises steps 1, 2, and/or 3. Preferably the method comprises steps 1 or 2. Preferably the method comprises steps (1 or 2) and (3 or 4). Preferably the method comprises steps (1 or 2) and 4. Preferably the method comprises steps 3 or 4.

These steps provide information regarding the likelihood of patient response. If based on step 1 or 2 the individual is classified as a likely responder to a TC4sub compound and a likely non-responder to a TC4-compound, the individual is preferably not treated with a TC4-compound. Instead the individual may be treated with an alternative MM treatment. In preferred embodiments the MM treatment comprises the use of a TC4sub compound. Accordingly, the disclosure also provides a TC4sub compound for use in the treatment of multiple myeloma, wherein the likelihood of response to the TC4sub compound is determined as disclosed herein.

If based on step 3 or 4 the individual is classified as a likely non-responder to a TC4sub compound and a likely responder to a TC4-compound, the individual is preferably not treated with TC4sub compound. Instead the individual is treated with an alternative MM treatment. In preferred embodiments the MM treatment comprises the use of a TC4-compound. Accordingly, the disclosure also provides a TC4-compound for use in the treatment of multiple myeloma, wherein the likelihood of response to a TC4-compound is determined as disclosed herein.

It is well within the purview of a skilled person to prepare suitable pharmaceutical compositions comprising a TC4-compound or a TC4sub compound. As is clear to a skilled person, treatment of an individual may include administration of such pharmaceutical compositions.

In some embodiments of the disclosure, kits are provided for use in diagnostic, research, and therapeutic applications. Preferably, the disclosure provides kits for classifying an individual with multiple myeloma based on the likelihood of response to treatment with an immunomodulatory drug (IMiD), wherein the kit comprises:

a) means for determining in a sample from said individual the level of expression of at least one marker selected from Table 11;

b) means for determining in a sample from said individual the presence of the t(4;14) translocation;

c) means for determining in a sample from said individual the level of expression of at least one marker in Table 3; and/or

d) means for determining in a sample from said individual the presence of the t(11;14) translocation;

Preferably, the means referred to in step a) or step b) comprise an array of probes, e.g., a microarray. Preferably, the array consists of probes that specifically detect markers selected from Table 1, Table 2, Table 3, Table 4, Table 11 and Table 12. Preferably, at least 5 probes, at least 10 probes, or at least 20 probes are present on the array. In some embodiments, the disclosure provides the use of one or more markers selected from Table 11 as a diagnostic for classifying an individual based on the likelihood of response to treatment with an IMiD, as disclosed herein.

Definitions

As used herein, “to comprise” and its conjugations is used in its non-limiting sense to mean that items following the word are included, but items not specifically mentioned are not excluded. In addition the verb “to consist” may be replaced by “to consist essentially of” meaning that a compound or adjunct compound as defined herein may comprise additional component(s) than the ones specifically identified, said additional component(s) not altering the unique characteristic of the invention.

The articles “a” and “an” are used herein to refer to one or to more than one (i.e., to at least one) of the grammatical object of the article.

The invention is further explained in the following examples. These examples do not limit the scope of the invention, but merely serve to clarify the invention.

EXAMPLES Example 1

GEP (gene expression profiling) has enabled the development of signatures, such as the EMC92/SKY92 signature¹⁴, and the GEP clusters (MS, MF, etc.).^(6,15) For prognostic purposes, GEP based markers have been shown to be more robust across cohorts compared to iFISH results.^(16,17) Consequently, they have been integrated into clinical guidelines and consensus papers¹⁸, and currently pave the way for risk stratified treatment approaches in MM. Five GEP markers (SKY92, virtual gain(1q), virtual t(14;16)/t(14;20), cluster CD2, MF cluster) have been previously identified, which distinguish patients with a survival benefit when treated with proteasome inhibitors²¹.

Here we applied GEP on samples from the HOVON-87/NMSG-18 study¹⁹ for comprehensive genetic subtyping. In the HOVON-87/NMSG-18 study, induction therapy with melphalan, prednisone and thalidomide, followed by thalidomide maintenance (MPT-T), was compared with melphalan, prednisone and lenalidomide, followed by lenalidomide maintenance (MPR-R). The data shows that patients that are identified to belong to the genetic subtype SKY92, virtual t(4;14), MS cluster, or iFISH t(4;14), have a survival benefit from Lenalidomide induction and maintenance treatment compared to thalidomide induction and maintenance treatment and therefore should be preferentially treated with a Lenalidomide regime. In other words, SKY92 positive patients should be treated with MPR-R rather than MPT-T. Conversely, virtual t(11;14) patients have a survival benefit from thalidomide induction and maintenance treatment compared to lenalidomide induction and maintenance treatment and therefore should be preferentially treated with a thalidomide regime. In other words, virtual t(11;14) positive cases should be treated with MPT-T rather than MPR-R.

Materials and Methods

The HOVON-87/NMSG-18 trial (EudraCTnr.: 2007-004007-34) is a phase 3 trial for elderly MM patients (age 65 and older, or age <65 and transplant in-eligible) in which induction therapy with melphalan, prednisone and thalidomide, followed by thalidomide maintenance, was compared with melphalan, prednisone and lenalidomide, followed by lenalidomide maintenance (MPT-T vs. MPR-R).

Interphase FISH on isolated CD138-positive plasma cells was performed according to the EMN guidelines (Ross et al., Haematolologica 2012 97:1272), in order to determine the presence of t(4;14) and t911:14).

Gene Expression Profiles (GEP) were assessed from n=143 samples enrolled in this HOVON-87/NMSG-18 trial using the MMprofiler. Out of these 143 patients, 83 were from the MPT-T arm, and 60 from the MPR-R arm. The GEP data were normalized as described in Van Vliet et al. 2014²⁰. Subsequently, SKY92 (=EMC92) scores were calculated as described in Kuiper et al 2012¹⁴. Briefly, the SKY92 is a summation of the weighted expression of 92 probe sets (see Table 1). This signature constitutes a linear model, expressed in the following formula:

$\begin{matrix} {{{SKY92}(x)} = {\sum\limits_{i = 1}^{92}\; {\beta_{i}x_{i}}}} & {{Equation}\mspace{14mu} 3} \end{matrix}$

where βi represents the weight factor of gene i, and x_(i) represents the expression level of gene i in a patient. Based on their SKY92 score, patients were split into two groups, those above the threshold of 0.7774 were classified as positive (High Risk), and those below the threshold as negative (Standard Risk)¹⁴. When using subsets of the 92 probesets, it is possible to keep the weights of that subset as provided in Table 1, and retrain a new threshold as the top 21.7% of the SKY92 scores, or to redistribute the weight of discarded genes to the remaining genes based on the covariance structure in the training set (HOVON-65/GMMG-HD4), and still use the existing threshold of 0.7774.

For the virtual t(4;14), virtual t(11;14), and MS cluster markers, classifiers have been trained that employ a selection of probe sets (see Table 2, 3, and 4) that enable the distinction of whether a subject does have that characteristic (positive or 1) or does not have that characteristic (negative or 0). Specifically, the Classification to Nearest Centroids method was used (ClaNC)²², known in the art as linear classifiers (nearest mean classifier, LDA, or similar). The method uses the mean and standard deviation of each class to classify a new patient. For a new patient, the normalized Euclidean distances are calculated to each of the two classes, as defined by:

$\begin{matrix} {{d_{0}(x)} = \sqrt{\sum\limits_{i = 1}^{N}\; \frac{\left( {x_{i} - m_{0,i}} \right)^{2}}{s_{0,i}^{2}}}} & {{Equation}\mspace{14mu} 1} \\ {{d_{1}(x)} = \sqrt{\sum\limits_{i = 1}^{N}\; \frac{\left( {x_{i} - m_{1,i}} \right)^{2}}{s_{1,i}^{2}}}} & {{Equation}\mspace{14mu} 2} \end{matrix}$

where x_(i) represents the expression level of gene i in a patient, N represents the total number of probe sets, m_(1,i) represents the mean of centroid 1 for gene i and s_(1,i) represents the standard deviation of centroid 1 for gene i. These values can be found per probe set for each marker in. Using d₁ and d₀, a patient is assigned to the nearest class. For example, when d₁=3 and d₀=1, the patient will be assigned to class 0 because distance d₁ is greater than distance d₀. See Example 1a below for a detailed description. When using subsets of the probesets provided in the Tables, the procedure remains the same, i.e. when only two probesets are used the formulas in Equation 1 and 2 are only applied using those two probesets. The evaluation of d0 and d1 remains the same.

Survival curves were plotted using the Kaplan-Meier method. The Cox proportional hazards model was used to assess Hazard Ratios (HR) between groups of patients.

TABLE 1 SKY92 probe sets and weights Probesets Beta Gene Symbol 200701_at −0.0210 NPC2 200775_s_at 0.0163 HNRNPK /// MIR7-1 200875_s_at 0.0437 MIR1292 /// NOP56 /// SNORD110 /// SNORD57 /// SNORD86 200933_x_at −0.0323 RPS4X 201102_s_at 0.0349 PFKL 201292_at −0.0372 TOP2A 201307_at 0.0165 SEPT11 201398_s_at −0.0254 TRAM1 201555_at −0.0052 MCM3 201795_at 0.0067 LBR 201930_at −0.0090 MCM6 202107_s_at 0.0225 MCM2 202322_s_at 0.0129 GGPS1 202532_s_at −0.0006 DHFR 202542_s_at 0.0870 AIMP1 202553_s_at 0.0054 SYF2 202728_s_at −0.1105 LTBP1 202813_at 0.0548 TARBP1 202842_s_at −0.0626 DNAJB9 202884_s_at 0.0714 PPP2R1B 203145_at −0.0002 SPAG5 204026_s_at 0.0046 ZWINT 204379_s_at 0.0594 FGFR3 205046_at 0.0087 CENPE 206204_at 0.0477 GRB14 207618_s_at 0.0746 BCS1L 208232_x_at −0.0493 NRG1 208667_s_at −0.0390 ST13 208732_at −0.0618 RAB2A 208747_s_at −0.0874 C1S 208904_s_at −0.0334 RPS28 208942_s_at −0.0997 SEC62 208967_s_at 0.0113 AK2 209026_x_at 0.0255 TUBB 209683_at −0.0561 FAM49A 210334_x_at 0.0175 BIRC5 211714_x_at 0.0221 TUBB 211963_s_at 0.0303 ARPC5 212055_at 0.0384 TPGS2 212282_at 0.0530 TMEM97 212788_x_at −0.0164 FTL 213002_at −0.0418 MARCKS 213007_at −0.0106 FANCI 213350_at 0.0056 RPS11 214150_x_at −0.0349 ATP6V0E1 214482_at 0.0861 ZBTB25 214612_x_at 0.0496 MAGEA6 215177_s_at −0.0768 ITGA6 215181_at −0.0342 CDH22 216473_x_at −0.0576 DUX2 /// DUX4 /// DUX4L2 /// DUX4L3 /// DUX4L4 /// DUX4L5 /// DUX4L6 /// DUX4L7 /// LOC100288627 /// LOC100288657 /// LOC652119 217548_at −0.0423 LOC100129502 217728_at 0.0773 S100A6 217732_s_at −0.0252 ITM2B 217824_at −0.0041 UBE2J1 217852_s_at 0.0008 ARL8B 218355_at 0.0116 KIF4A 218365_s_at 0.0035 DARS2 218662_s_at −0.0176 NCAPG 219510_at −0.0097 POLQ 219550_at 0.0559 ROBO3 220351_at 0.0420 CCRL1 221041_s_at −0.0520 SLC17A5 221606_s_at 0.0208 HMGN5 221677_s_at 0.0126 DONSON 221755_at 0.0396 EHBP1L1 221826_at 0.0200 ANGEL2 222154_s_at 0.0154 SPATS2L 222680_s_at 0.0205 DTL 222713_s_at 0.0278 FANCF 223381_at −0.0070 NUF2 223811_s_at 0.0556 GET4 /// SUN1 224009_x_at −0.0520 DHRS9 225366_at 0.0140 PGM2 225601_at 0.0750 HMGB3 226217_at −0.0319 SLC30A7 226218_at −0.0644 IL7R 226742_at −0.0345 SAR1B 228416_at −0.0778 ACVR2A 230034_x_at −0.0330 MRPL41 231210_at 0.0093 C11orf85 231738_at 0.0686 PCDHB7 231989_s_at 0.0730 61E3.4 /// LOC100132247 /// LOC100271836 /// LOC100652992 /// LOC613037 /// LOC728888 /// NPIPL3 /// SLC7A5P1 /// SMG1P1 233399_x_at −0.0184 TMED10P1 /// ZNF252 233437_at 0.0446 GABRA4 238116_at 0.0661 DYNLRB2 238662_at 0.0490 ATPBD4 238780_s_at −0.0529 — 239054_at −0.1088 SFMBT1 242180_at −0.0585 TSPAN16 243018_at 0.0407 — 38158_at 0.0423 ESPL1 AFFX-HUMISGF3A/ 0.0525 STAT1 /// STAT1 M97935_MA_at

Positive beta values (i.e., weight values) indicate that increased expression of said gene over a reference value indicates a positive contribution towards the SKY92 score, as a consequence a larger chance of being above the threshold, or rather that the patient likely responds to MPR-R and does not likely respond to MPT-T. Conversely, positive beta values indicate that decreased expression of said gene over a reference value indicates a negative, contribution towards the SKY92 score, as a consequence a larger chance of being below the threshold, or rather that the patient likely responds to MPR-R and to MPT-T.

Negative beta values indicate that decreased expression of said gene over a reference value indicates a positive contribution towards the SKY92 score, as a consequence a larger chance of being above the threshold, or rather that the patient likely responds to MPR-R and does not likely respond to MPT-T. Conversely, negative beta values indicate that increased expression of said gene over a reference value indicates a negative, contribution towards the SKY92 score, as a consequence a larger chance of being below the threshold or rather that the patient likely responds to MPR-R and to MPT-T.

TABLE 2 Virtual t(4;14) probe sets t(4:14) negative t(4:14) positive Gene Probeset m0 s0 m1 s1 symbol 204379_s_at −0.25462 0.530071 1.537652 1.496546 FGFR3 205131_x_at −0.27838 0.842607 1.175988 0.843791 CLEC11A 205830_at −0.19975 0.870945 1.092877 0.621865 CLGN 211709_s_at −0.22553 0.727177 1.384303 1.030579 CLEC11A 212148_at −0.39946 0.769413 1.220048 0.760111 PBX1 212151_at −0.36575 0.844361 1.168239 0.81162 PBX1 212813_at −0.26267 0.766274 1.243431 0.814053 JAM3 217867_x_at −0.20611 0.822701 1.49116 0.815068 BACE2 221261_x_at −0.14034 0.952932 1.158952 0.395102 MAGED4 /// MAGED4B /// SNORA11D /// SNORA11E 222258_s_at −0.26969 0.87769 1.168436 0.604416 SH3BP4 222777_s_at −0.3524 0.622727 1.631826 0.949852 WHSC1 222778_s_at −0.42306 0.627198 1.518182 0.980568 WHSC1 223313_s_at −0.10729 0.940715 1.210379 0.540292 MAGED4 /// MAGED4B /// SNORA11D /// SNORA11E 223472_at −0.34184 0.740682 1.200404 0.998955 WHSCI 223822_at −0.20027 0.797071 1.544708 0.887078 SUSD4 227084_at −0.26801 0.876058 1.259394 0.786791 DTNA 227290_at −0.22604 0.8319 1.259377 0.703202 LOC100509498 227434_at −0.17149 0.806988 1.410106 0.837651 WBSCR17 227692_at −0.2828 0.818394 1.232278 0.846703 GNAI1

TABLE 3 Virtual t(11; 14) probe sets t(11:14) negative t(11:14) positive Gene Probeset m0 s0 m1 s1 symbol 208711_s_at −0.32007 0.740631 1.443502 0.451984 CCND1 208712_at −0.19407 0.841579 1.155986 0.262512 CCND1 235518_at −0.24236 0.920001 1.162315 0.70785 SLC8A1

TABLE 4 MS cluster probe sets Non-MS MS Gene Probeset m0 s0 m1 s1 symbol 1553105_s_at −0.17941 0.863533 1.574718 0.791573 DSG2 1557780_at −0.18413 0.839616 1.606693 0.826452 — 204066_s_at −0.15171 0.92486 1.358666 0.453127 AGAP1 204379_s_at −0.23285 0.6569 1.898836 1.376235 FGFR3 205559_s_at −0.16509 0.890517 1.471314 0.572184 PCSK5 211709_s_at −0.18311 0.879425 1.524201 0.638017 CLEC11A 212190_at −0.16699 0.896555 1.437085 0.646261 SERPINE2 212686_at −0.16492 0.926994 1.357986 0.410138 PPM1H 212771_at −0.1518 0.940981 1.381678 0.306368 FAM171A1 214156_at −0.19327 0.893119 1.489983 0.543743 MYRIP 217867_x_at −0.17452 0.8793 1.609823 0.392832 BACE2 217901_at −0.18751 0.880607 1.543465 0.614766 DSG2 222258_s_at −0.16574 0.922573 1.384121 0.516469 SH3BP4 222777_s_at −0.23881 0.712218 2.147318 0.565077 WHSC1 222778_s_at −0.2283 0.705499 2.120403 0.661262 WHSC1 223472_at −0.18119 0.846357 1.654937 0.632111 WHSC1 223822_at −0.18887 0.832638 1.590486 0.823419 SUSD4 227084_at −0.18523 0.880019 1.536949 0.498332 DTNA 227692_at −0.16209 0.891145 1.469242 0.658789 GNAI1 238116_at −0.18331 0.845629 1.654202 0.694305 DYNLRB2

Example 1a: Method for Determining Whether a Subject Belongs to the MS Cluster Using the ClaNC Method

Fictitious data (fable 5) is used as an example for the classification method, using 2 genes for simplicity, to predict whether a sample belongs to MS or non-MS type. In the column “Example patient data”, the measured expression levels are shown for both genes.

TABLE 5 m and s values for the first two probe sets of the MS cluster and the example patient data used in example 1. All values are rounded to 3 decimals for the purpose of the example. The last two columns are the results of the classification process. Example Non-MS MS patient Probe set m0 s0 m1 s1 data d₀ d₁ Probe set 1 −0.127 0.868 1.936 0.939 0.121 1.015 2.359 Probe set 2 −0.084 0.936 1.707 0.650 0.828

The d₀ and the d₁ were calculated using the values in Table, and Equations 1 and 2. The worked out formulas are shown in Equation and Equation.

$\begin{matrix} {{d_{0}(x)} = {\sqrt{\sum\limits_{i = 1}^{N}\; \frac{\left( {x_{i} - m_{0,i}} \right)^{2}}{s_{0,i}^{2}}} = {\sqrt{\frac{\left( {x_{1} - m_{0,1}} \right)^{2}}{s_{0,1}^{2}} + \frac{\left( {x_{2} - m_{0,2}} \right)^{2}}{s_{0,2}^{2}}} = {\sqrt{\frac{\left( {{{.121}--}0.127} \right)^{2}}{0.868^{2}} + \frac{\left( {{0.828--}0.084} \right)^{2}}{0.936^{2}}} = {\sqrt{0.082 + 0.949} = 1.015}}}}} & {{Equation}\mspace{14mu} 4} \\ {{d_{1}(x)} = {\sqrt{\sum\limits_{i = 1}^{N}\; \frac{\left( {x_{i} - m_{1,i}} \right)^{2}}{s_{1,i}^{2}}} = {\sqrt{\frac{\left( {x_{1} - m_{1,1}} \right)^{2}}{s_{1,1}^{2}} + \frac{\left( {x_{2} - m_{1,2}} \right)^{2}}{s_{1,2}^{2}}} = {\sqrt{\frac{\left( {0.121 - 1.936} \right)^{2}}{0.939^{2}} + \frac{\left( {0.828 - 1.707} \right)^{2}}{0.650^{2}}} = {\sqrt{3.736 + 1.829} = 2.359}}}}} & {{Equation}\mspace{14mu} 5} \end{matrix}$

The next step is to compare the d₀ and d₁ values. When d₀<d₁ is true, the new patient will be assigned to class 0. If d₀>d₁ is true, the new patient will be assigned to class 1. Here d₀<d₁ is true, which means the new patient is placed in the 0 class (non-MS).

Example 1b: Method for Determining Whether a Subject Belongs to the SKY92 Positive or SKY92 Negative Group

Fictitious data (fable 6) is used as an example for the SKY92 classification method, to determine whether a sample belongs to SKY92 positive or SKY92 negative. In the column “Example patient data”, the measured expression levels x_(i) are shown for all 92 genes. For each gene the x_(i) is multiplied by the βi, for which the result is provided in a column in Table 6. Subsequently those values are summed up, providing the SKY92(x)=−0.4455. This value is then compared to the threshold of 0.7774, and since it is lower than the threshold, the patient is determined to be SKY92 negative.

TABLE 6 Fictitious data (x) from an example patient for all 92 genes from the SKY92 signature, the betas of all genes, the result obtained after multiplication of betas and x_(i) values, and at the bottom of the Table the summation of all those values (SKY92(x)). x (Example patient Beta * Probesets Beta data) x 200701_at −0.0210 0.1049 −0.0022 200775_s_at 0.0163 0.7223 0.0118 200875_s_at 0.0437 2.5855 0.1130 200933_x_at −0.0323 −0.6669 0.0215 201102_s_at 0.0349 0.1873 0.0065 201292_at −0.0372 −0.0825 0.0031 201307_at 0.0165 −1.9330 −0.0319 201398_s_at −0.0254 −0.4390 0.0111 201555_at −0.0052 −1.7947 0.0093 201795_at 0.0067 0.8404 0.0056 201930_at −0.0090 −0.8880 0.0080 202107_s_at 0.0225 0.1001 0.0023 202322_s_at 0.0129 −0.5445 −0.0070 202532_s_at −0.0006 0.3035 −0.0002 202542_s_at 0.0870 −0.6003 −0.0522 202553_s_at 0.0054 0.4900 0.0026 202728_s_at −0.1105 0.7394 −0.0817 202813_at 0.0548 1.7119 0.0938 202842_s_at −0.0626 −0.1941 0.0122 202884_s_at 0.0714 −2.1384 −0.1527 203145_at −0.0002 −0.8396 0.0002 204026_s_at 0.0046 1.3546 0.0062 204379_s_at 0.0594 −1.0722 −0.0637 205046_at 0.0087 0.9610 0.0084 206204_at 0.0477 0.1240 0.0059 207618_s_at 0.0746 1.4367 0.1072 208232_x_at −0.0493 −1.9609 0.0967 208667_s_at −0.0390 −0.1977 0.0077 208732_at −0.0618 −1.2078 0.0746 208747_s_at −0.0874 2.9080 −0.2542 208904_s_at −0.0334 0.8252 −0.0276 208942_s_at −0.0997 1.3790 −0.1375 208967_s_at 0.0113 −1.0582 −0.0120 209026_x_at 0.0255 −0.4686 −0.0119 209683_at −0.0561 −0.2725 0.0153 210334_x_at 0.0175 1.0984 0.0192 211714_x_at 0.0221 −0.2779 −0.0061 211963_s_at 0.0303 0.7015 0.0213 212055_at 0.0384 −2.0518 −0.0788 212282_at 0.0530 −0.3538 −0.0188 212788_x_at −0.0164 −0.8236 0.0135 213002_at −0.0418 −1.5771 0.0659 213007_at −0.0106 0.5080 −0.0054 213350_at 0.0056 0.2820 0.0016 214150_x_at −0.0349 0.0335 −0.0012 214482_at 0.0861 −1.3337 −0.1148 214612_x_at 0.0496 1.1275 0.0559 215177_s_at −0.0768 0.3502 −0.0269 215181_at −0.0342 −0.2991 0.0102 216473_x_at −0.0576 0.0229 −0.0013 217548_at −0.0423 −0.2620 0.0111 217728_at 0.0773 −1.7502 −0.1353 217732_s_at −0.0252 −0.2857 0.0072 217824_at −0.0041 −0.8314 0.0034 217852_s_at 0.0008 −0.9792 −0.0008 218355_at 0.0116 −1.1564 −0.0134 218365_s_at 0.0035 −0.5336 −0.0019 218662_s_at −0.0176 −2.0026 0.0352 219510_at −0.0097 0.9642 −0.0094 219550_at 0.0559 0.5201 0.0291 220351_at 0.0420 −0.0200 −0.0008 221041_s_at −0.0520 −0.0348 0.0018 221606_s_at 0.0208 −0.7982 −0.0166 221677_s_at 0.0126 1.0187 0.0128 221755_at 0.0396 −0.1332 −0.0053 221826_at 0.0200 −0.7145 −0.0143 222154_s_at 0.0154 1.3514 0.0208 222680_s_at 0.0205 −0.2248 −0.0046 222713_s_at 0.0278 −0.5890 −0.0164 223381_at −0.0070 −0.2938 0.0021 223811_s_at 0.0556 −0.8479 −0.0471 224009_x_at −0.0520 −1.1201 0.0582 225366_at 0.0140 2.5260 0.0354 225601_at 0.0750 1.6555 0.1242 226217_at −0.0319 0.3075 −0.0098 226218_at −0.0644 −1.2571 0.0810 226742_at −0.0345 −0.8655 0.0299 228416_at −0.0778 −0.1765 0.0137 230034_x_at −0.0330 0.7914 −0.0261 231210_at 0.0093 −1.3320 −0.0124 231738_at 0.0686 −2.3299 −0.1598 231989_s_at 0.0730 −1.4491 −0.1058 233399_x_at −0.0184 0.3335 −0.0061 233437_at 0.0446 0.3914 0.0175 238116_at 0.0661 0.4517 0.0299 238662_at 0.0490 −0.1303 −0.0064 238780_s_at −0.0529 0.1837 −0.0097 239054_at −0.1088 −0.4762 0.0518 242180_at −0.0585 0.8620 −0.0504 243018_at 0.0407 −1.3617 −0.0554 38158_at 0.0423 0.4550 0.0192 AFFX- 0.0525 −0.8487 −0.0446 HUMISGF3A/M97935_MA_at SKY92(x) = Sum(Beta * x) = −0.4455

Results

Using the SKY92 signature 22/143 patients were identified as high risk (15.4%). The median overall survival (OS) for high risk patients was 21 months, compared to 53 months for standard risk patients (hazard ratio (HR): 2.9 (95% confidence interval (CI): 1.6-5.4; p=5.6×10-4)). The median progression free survival (PFS) in the high risk and standard risk groups were 12 months and 23 months, respectively (HR: 2.2 (95% CI: 1.4-3.7; p=1.2×10-3)). See FIG. 1. Combining the 2 SKY92 groups and 2 treatment arms results in 4 groups of patients. As can be seen in FIG. 2, for OS there is a significantly different Hazard Ratio between SKY92 SR and SKY92 HR in the MPT-T arm (HR=4.1, p=0.0002), but not in the MPR-R arm (HR=1.35, p=0.63). Comparing the two treatment arms in the SKY92 High Risk group shows that those patients have longer Overall Survival when given MPR-R (HR=3.4, p=0.06). Conversely, in the SKY92 SR group there is no difference between the treatment arm (HR=1.0, p=0.93). These observations support the use of the SKY92 marker to identify a subgroup that benefits from a specific treatment over another treatment whereas the negative cases do not have that treatment benefit. Therefore, the marker can be used to predict specific therapy effectiveness in a subgroup of patients i.e. as a means to determine an MM patient's preferential treatment.

The “Virtual t(4;14)” marker is highly congruent with iFISH t(4;14), and is associated with the MS cluster. These markers are not prognostic in this clinical study, as there is no survival difference between the positive and negative patient groups for this marker (respectively: HR=1.68, p=0.18; HR=1.34, p=0.47; HR=1.63, p=0.23). However, when splitting the positive patients by treatment arm, there is a significant OS advantage when they are treated with MPR-R as opposed to MPT-T (respectively:

HR=0.091, p=0.032; HR=0.093, p=0.038; HR=0.107, p=0.045), whereas there is no difference in the marker negative group (respectively: HR=0.889, p=0.690; HR=0.760, p=0.384; HR=0.915, p=0.759). See FIG. 3. These observations support the use of the Virtual t(4;14), iFISH t(4;14), and the MS cluster as predictive marker, i.e. as a means to determine an MM patient's preferential treatment. Positive cases for either of these three markers iFISH t(4;14), virtual t(4;14) or the MS cluster have a benefit from MPR-R treatment over MPT-T treatment whereas the patients negative for these markers do not have a survival difference when treated with either of the two treatments.

The Virtual t(11;14) marker is congruent with iFISH t(11;14), though neither is prognostic with HR=1.04, p=0.92, and HR=0.66, p=0.26, respectively, between the positive/negative groups. However, when splitting by treatment arm, there is an indication that Virtual t(11;14) positive patients have an OS advantage when treated with MPT-T over MPR-R, HR=5.7, p=0.043. At the same time, in the Virtual t(11;14) negative group there is an indication that the MPR-R treatment outperforms the MPT-T treatment at HR=0.59, p=0.086. See FIG. 4. These observations support the use of the Virtual t(11;14) and iFISH t(11;14) as a predictive marker, i.e. as a means to determine an MM patient's preferential treatment. Positive cases for the t(11;14) marker have a benefit from MPT-T treatment over MPR-R treatment, whereas the negative cases for this marker have a benefit from MPR-R treatment over MPT-T treatment.

Table 7 shows the overlap of the samples. For example, Table 7a shows that there are 9 patients which are virtual t(4;14) positive and at the same time SKY92 High Risk. Of the 143 samples, iFISH t(4:14) status was determined in 128 of the samples (Table 7b) and iFISH t(11;14) status was determined in 107 samples (fable 7c). As expected, the overlap between iFiSH and virtual translocations is high. The overlap between the t(4;14) marker and the MS cluster is also very high. Approximately half of the t(4;14) cases are also SKY92 High Risk. On the other hand, the overlap between t(11;14), and SKY92 High Risk is limited. The t(11;14) and t(4;14) translocations are mutually exclusive, which is in line with previous findings.

Table 7: Tables indicating pairwise overlap of the different markers, overlap between the same marker (diagonal entries) indicates the number of positives for that marker.

Tables 7a and 7b

128 H87 patients with iFISH t(4; 14) SKY92 Cluster Virtual Virtual iFISH High Risk MS t(4; 14) t(11; 14) t(4; 14) SKY92 High Risk 21 9 9 1 8 Cluster MS 9 15 15 0 12 Virtual t(4; 14) 9 15 16 0 13 Virtual t(11; 14) 1 0 0 21 0

All 143 H87 samples SKY92 Cluster Virtual Virtual High Risk MS t(4; 14) t(11; 14) SKY92 High Risk 22 9 9 2 Cluster MS 9 15 15 0 Virtual t(4; 14) 9 15 16 0 Virtual t(11; 14) 2 0 0 25

TABLE 7c 107 H87 patients with iFISH t(11; 14) SKY92 Cluster Virtual Virtual iFISH High Risk MS t(4; 14) t(11; 14) t(11; 14) SKY92 High Risk 14 3 3 1 2 Cluster MS 3 8 8 0 0 Virtual t(4; 14) 3 8 8 0 0 Virtual t(11; 14) 1 0 0 20 13

TABLE 8A Indicates pairwise overlap in terms of probesets used in the different GEP signatures. Overlap between the same marker (diagonal entries) indicates the number of probesets in the signature for that marker. Overlap Probesets in signatures SKY92 Cluster Virtual Virtual High Risk MS t(4; 14) t(11; 14) SKY92 High Risk 92 2 1 0 Cluster MS 2 20 10 0 Virtual t(4; 14) 1 10 19 0 Virtual t(11; 14) 0 0 0 3

Conclusion

In conclusion, the SKY92 signature is a useful prognostic marker to identify a high-risk subgroup in the elderly population. Moreover, MM patients with SKY92 High Risk, Virtual t(4;14), iFISH t(4;14), or MS cluster characteristics have improved Overall Survival when treated with MPR-R instead of MPT-T. Conversely, MM patients with Virtual t(11;14) have an OS advantage when treated with MPT-T.

Example 2

A further analysis was performed to demonstrate that subsets of markers from Tables 1-4 are predictive of patient response. In a specific embodiment, all single probesets and all pluralities of subsets of the 20, 19, 92, or 3 probesets from the Tables 1-4 can be employed. For each marker, the number of possible subsets was calculated using the binomial coefficient, defined as n!/((n−k)! k!). This is the number of combinations of n items taken k at a time. For example, from the list of 92 (n) probesets from SKY92, there are 4186 subsets of 2 (k). Table 8B shows the number of unique subsets that can be taken for each of the markers. For each of the markers all subsets of 1, 2, 3, and 4 probesets were evaluated. This was done using the data from the 143 patients analyzed in the HOVON-87/NMSG-18 dataset.

TABLE 8B The amount of subsets of a specific size that can be selected from the total number of probesets in each of the four signatures. Probesets in Subsets Subsets Subsets Subsets Signature Signature of 1 of 2 of 3 of 4 SKY92 92 92 4186 125580 2794155 Virtual t(4; 14) 19 19 171 969 3876 MS Cluster 20 20 190 1140 4845 Virtual t(11; 14) 3 3 3 1 NA

For example, for the SKY92 signature, all 4186 subsets of 2 probesets were tested. That is, when two probesets were tested, for each of the 143 samples in the HOVON-87/NMSG-18 the equation 3 was applied. In this case the summation then goes to two instead of 92. Subsequently, the 143 SKY92(x) scores were sorted, and the top 22 (=the same amount as when all 92 probesets are used) were taken as SKY92 High Risk. This ensures that the same fraction of High Risk cases are identified, as the thresholds needs to be adjusted to be applicable for the subset of probesets. Next, within those 22 SKY92 High Risk patients, a Cox Proportional Hazards model was applied using the Treatment arm as covariate, providing a Hazard Ratio g C4-/TC4sub, in the same fashion as shown in FIG. 2). All Hazard Ratios were collected, and are shown in FIG. 5.

For example, for the Virtual t(4;14) marker, all 969 subsets of 3 probesets were tested. That is, when three probesets were tested, for each of the 143 samples in the HOVON-87/NMSG-18, the equation 1 and 2 were applied. In this case the summation then goes to three instead of 19. Next, for each of the 143 samples in the HOVON-87/NMSG18 the resulting d0 and d1 were compared. Samples where d1 is smaller were classified as positive for that particular marker. Next, within those Virtual t(4;14) positive patients, a Cox Proportional Hazards model was applied using the Treatment arm as covariate, providing a Hazard Ratio (TC4-/TC4sub, in the same fashion as shown in FIG. 3, although in FIG. 3 the ratio is inverted: i.e. TC4sub/TC4-). All Hazard Ratios were collected, and are shown in FIG. 5. However, in this analysis the comparison of treatment was opposite to that shown in FIG. 3. Otherwise stated, the HRs shown in FIG. 3 could be considered as 1/HR when compared to FIG. 5.

As can be clearly seen in FIG. 5 and Tables 9 and 10, the majority of subsets (up to 100%) of each of the 4 signatures work, and indicate a benefit in terms of Overall Survival in favour of MPR-R for the SKY92, Virtual t(4;14), and MS cluster, and a benefit in terms of Overall Survival in favour of MPT-T for the Virtual t(11;14) signature.

TABLE 9 Number of tested subsets that had a Hazard Ratio (TC4-/TC4sub) larger than 1 or smaller than 1 (i.e. in the same direction as when using all probesets). Subsets Subsets Subsets Subsets All Signature of 1 of 2 of 3 of 4 probesets HR > 1 SKY92 72 3351 103244 2332090 1 Virtual t(4; 14) 18 168 969 3876 1 MS Cluster 20 190 1140 4845 1 HR < 1 Virtual t(11; 14) 3 3 1 1 1

TABLE 10 Percentage of all tested subsets that had a Hazard Ratio (TC4-/TC4sub) larger than 1 or smaller than 1 (i.e. in the same direction as when using all probesets). Subsets Subsets Subsets Subsets All Signature of 1 of 2 of 3 of 4 probesets HR > 1 SKY92 78.26% 80.05% 82.21% 83.46% 100.00% Virtual t(4; 14) 94.74% 98.25% 100.00% 100.00% 100.00% MS Cluster 100.00% 100.00% 100.00% 100.00% 100.00% HR < 1 Virtual t(11; 14) 100.00% 100.00% 100.00% 100.00% 100.00%

Table 9 shows that 72 markers from Table 1, 18 markers from Table 2 and all markers from Table 4 can, when used individually, identify patients with an improved OS for MPR-R (HR>1, indicating that MPT-T has lower OS than MPR-R). Table 11 shows an overview of the combined unique list of the 98 probesets. Table 9 also shows that all markers from Table 3 can, when used individually, identify patients with an improved OS for MPT-T (HR<1, indicating that MPT-T has higher OS than MPR-R). Table 12 shows the additional 21 probesets from Tables 1-4, which were not part of Table 11.

TABLE 11 Overview and annotation for 98 probe sets that are individually predictive for improved OS on MPR-R when compared with MPT-T. Gene Symbol and Gene Title information were retrieved from the Affymetrix NetAffx website (https://www.affymetrix.com/estore/analysis/index.affx) on Jan. 26^(th), 2016. Probeset Signature Gene Symbol Gene Title 204379_s_al Virtual FGFR3 fibroblast growth factor t(4; 14), receptor 3 Cluster MS, SKY92 211709_s_at Virtual CLEC11A C-type lectin domain t(4; 14), family 11, member A Cluster MS 217867_x_at Virtual BACE2 beta-site APP-cleaving t(4; 14), enzyme 2 Cluster MS 222258_s_at Virtual SH3BP4 SH3-domain binding t(4; 14), protein 4 Cluster MS 222777 s at Virtual WHSC1 Wolf-Hirschhorn t(4; 14), syndrome candidate 1 Cluster MS 222778_s_at Virtual WHSC1 Wolf-Hirschhorn t(4; 14), syndrome candidate 1 Cluster MS 223472_at Virtual WHSC1 Wolf-Hirschhorn t(4; 14), syndrome candidate 1 Cluster MS 223822_at Virtual SUSD4 sushi domain containing t(4; 14), 4 Cluster MS 227084_at Virtual DTNA dystrobrevin, alpha t(4; 14), Cluster MS 227692_at Virtual GNAI1 guanine nucleotide t(4; 14), binding protein (G Cluster MS protein), alpha inhibiting activity polypeptide 1 238116_at Cluster MS, DYNLRB2 dynein, light chain, SKY92 roadblock-type 2 1553105_s_at Cluster MS DSG2 desmoglein 2 1557780_at Cluster MS — — 200701_at SKY92 NPC2 Niemann-Pick disease, type C2 200875_s_at SKY92 MIR1292 /// microRNA 1292 /// NOP56 /// NOP56 SNORD110 /// ribonucleoprotein /// SNORD57 /// small nucleolar RNA, SNORD86 C/D box 110 /// small nucleolar RNA, C/D box 57 /// small nucleolar RNA, C/D box 86 200933_x_at SKY92 RPS4X ribosomal protein S4, X- linked 201307_at SKY92 SEP11 septin 11 201398_s_at SKY92 TRAM1 translocation associated membrane protein 1 201555_at SKY92 MCM3 minichromosome maintenance complex component 3 201795_at SKY92 LBR lamin B receptor 202107_s_at SKY92 MCM2 minichromosome maintenance complex component 2 202532_s_at SKY92 DHFR dihydrofolate reductase 202542_s_at SKY92 AIMP1 aminoacyl tRNA synthetase complex- interacting multifunctional protein 1 202553_s_at SKY92 SYF2 SYF2 pre-mRNA- splicing factor 202728_s_at SKY92 LTBP1 latent transforming growth factor beta binding protein 1 202813_at SKY92 TARBP1 TAR (HIV-1) RNA binding protein 1 202842_s_at SKY92 DNAJB9 DnaJ (Hsp40) homolog, subfamily B, member 9 202884_s_at SKY92 PPP2R1B protein phosphatase 2, regulatory subunit A, beta 203145_at SKY92 SPAG5 sperm associated antigen 5 204026_s_at SKY92 ZWINT ZW10 interacting kinetochore protein 204066_s_at Cluster MS AGAP1 ArfGAP with GTPase domain, ankyrin repeat and PH domain 1 205046_at SKY92 CENPE centromere protein E, 312 kDa 205131_x_at Virtual CLEC11A C-type lectin domain t(4; 14) family 11, member A 205559_s_at Cluster MS PCSK5 proprotein convertase subtilisin/kexin type 5 205830_at Virtual CLGN calmegin t(4; 14) 206204_at SKY92 GRB14 growth factor receptor- bound protein 14 207618_s_at SKY92 BCS1L BC1 (ubiquinol- cytochrome c reductase) synthesis-like 208232_x_at SKY92 NRG1 neuregulin 1 208667_s_at SKY92 ST13 suppression of tumorigenicity 13 (colon carcinoma) (Hsp70 interacting protein) 208732_at SKY92 RAB2A RAB2A, member RAS oncogene family 208747_s_at SKY92 C1S complement component 1, s subcomponent 208904_s_at SKY92 RPS28 ribosomal protein S28 208942_s_at SKY92 SEC62 SEC62 homolog (S. cerevisiae) 208967_s_at SKY92 AK2 adenylate kinase 2 209026_x_at SKY92 TUBB tubulin, beta class I 210334_x_at SKY92 BIRC5 baculoviral IAP repeat containing 5 211714_x_at SKY92 TUBB tubulin, beta class I 211963_s_at SKY92 ARPC5 actin related protein 2/3 complex, subunit 5, 16 kDa 212055_at SKY92 TPGS2 tubulin polyglutamylase complex subunit 2 212148_at Virtual PBX1 pre-B-cell leukemia t(4; 14) homeobox 1 212151_at Virtual PBX1 pre-B-cell leukemia t(4; 14) homeobox 1 212190_at Cluster MS SERPINE2 serpin peptidase inhibitor, clade E (nexin, plasminogen activator inhibitor type 1), member 2 212282 at SKY92 TMEM97 transmembrane protein 97 212686_at Cluster MS PPM1H protein phosphatase, Mg2+/Mn2+ dependent, 1H 212771_at Cluster MS FAM171A1 family with sequence similarity 171, member A1 212788_x_at SKY92 FTL ferritin, light polypeptide 212813_at Virtual JAM3 junctional adhesion t(4; 14) molecule 3 213002_at SKY92 MARCKS myristoylated alanine- rich protein kinase C substrate 213007_at SKY92 FANCI Fanconi anemia, complementation group I 213350_at SKY92 RPS11 ribosomal protein S11 214156_at Cluster MS MYRIP myosin VIIA and Rab interacting protein 215177_s_at SKY92 ITGA6 integrin, alpha 6 215181_at SKY92 CDH22 cadherin 22, type 2 217548_at SKY92 ARPIN actin-related protein 2/3 complex inhibitor 217728_at SKY92 S100A6 S100 calcium binding protein A6 217732_s_at SKY92 ITM2B integral membrane protein 2B 217824_at SKY92 UBE2J1 ubiquitin-conjugating enzyme E2, J1 217852_s_at SKY92 ARL8B ADP-ribosylation factor- like 8B 217901_at Cluster MS DSG2 desmoglein 2 218365_s_at SKY92 DARS2 aspartyl-tRNA synthetase 2, mitochondrial 219510_at SKY92 POLQ polymerase (DNA directed), theta 219550_at SKY92 ROBO3 roundabout, axon guidance receptor, homolog 3 (Drosophila) 221041_s_at SKY92 SLC17A5 solute carrier family 17 (acidic sugar transporter), member 5 221261_x_at Virtual MAGED4 /// melanoma antigen t(4; 14) MAGED4B /// family D, 4 /// melanoma SNORA11D /// antigen family D, 4B /// SNORA11E small nucleolar RNA, H/ACAbox 11D /// small nucleolar RNA, H/ACA box 11E 221606_s_at SKY92 HMGN5 high mobility group nucleosome binding domain 5 221755_at SKY92 EHBP1L1 EH domain binding protein 1-like 1 222154_s_at SKY92 SPATS2L spermatogenesis associated, serine-rich 2-like 222680_s_at SKY92 DTL denticleless E3 ubiquitin protein ligase homolog (Drosophila) 222713_s_at SKY92 FANCF Fanconi anemia, complementation group F 224009_x_at SKY92 DHRS9 dehydrogenase/reductase (SDR family) member 9 225366_at SKY92 PGM2 phosphoglucomutase 2 225601_at SKY92 HMGB3 high mobility group box 3 226217_at SKY92 SLC30A7 solute carrier family 30 (zinc transporter), member 7 226218_at SKY92 IL7R interleukin 7 receptor 226742_at SKY92 SAR1B secretion associated, Ras related GTPase 1B 227290_at Virtual CDYL2 chromodomain protein, t(4; 14) Y-like 2 227434_at Virtual WBSCR17 Williams-Beuren t(4; 14) syndrome chromosome region 17 230034_x_at SKY92 MRPL41 mitochondrial ribosomal protein L41 231210_at SKY92 C11orf85 chromosome 11 open reading frame 85 231738_at SKY92 PCDHB7 protocadherin beta 7 231989_s_at SKY92 LOC101060604 /// putative L-type amino LOC101929910 /// acid transporter 1-like LOC102725125 /// protein IMAA-like /// LOC613037 /// nuclear pore complex- NPIPA5 /// interacting protein NPIPB3 /// family member B4-like /// NPIPB4 /// serine/threonine- NPIPB5 /// protein kinase SMG1- SLC7A5P1 /// like /// nuclear pore SMG1P1 /// complex interacting SMG1P3 protein pseudogene /// nuclear pore complex interacting protein family, member A5 /// nuclear pore complex interacting protein family, member B3 /// nuclear pore complex interacting protein family, member B4 /// nuclear pore complex interacting protein family, member B5 /// solute carrier family 7 (amino acid transporter light chain, L system), member 5 pseudogene 1 /// SMG1 pseudogene 1 /// SMG1 pseudogene 3 233399_x_at SKY92 ZNF252P zinc finger protein 252, pseudogene 233437_at SKY92 GABRA4 gamma-aminobutyric acid (GABA) A receptor, alpha 4 238662_at SKY92 DPH6 diphthamine biosynthesis 6 239054_at SKY92 SFMBT1 Scm-like with four mbt domains 1 243018_at SKY92 RP11-1L12.3 — 38158_at SKY92 ESPL1 extra spindle pole bodies homolog 1 (S. cerevisiae) AFFX- SKY92 STAT1 signal transducer and HUMISGF3A/M97935_MA_at activator of transcription 1, 91 kDa

TABLE 12 Gene Symbol and Gene Title information were retrieved from the Affymetrix NetAffx website (https://www.affymetrix.com/estore/analysis/index.affx) on Jan. 26^(th), 2016. Probeset Signature Gene Symbol Gene Title 200775_s_at SKY92 HNRNPK heterogeneous nuclear ribonucleoprotein K 201102_s_at SKY92 PFKL phosphofructokinase, liver 201292_at SKY92 TOP2A topoisomerase (DNA) II alpha 170 kDa 201930_at SKY92 MCM6 minichromosome maintenance complex component 6 202322_s_at SKY92 GGPS1 geranylgeranyl diphosphate synthase 1 209683_at SKY92 FAM49A family with sequence similarity 49, member A 214150_x_at SKY92 ATP6V0E1 ATPase, H+ transporting, lysosomal 9 kDa, V0 subunit e1 214482_at SKY92 ZBTB25 zinc finger and BTB domain containing 25 214612_x_at SKY92 MAGEA6 melanoma antigen family A, 6 216473_x_at SKY92 DBET /// DUX4 /// D4Z4 binding element transcript DUX4L1 /// DUX4L2 /// (non-protein coding) /// double DUX4L24 /// homeobox 4 /// double homeobox 4 DUX4L3 /// DUX4L4 /// like 1 /// double homeobox 4 like 2 /// DUX4L5 /// double homeobox 4 like 24 /// DUX4L6 /// DUX4L7 /// double homeobox 4 like 3 /// DUX4L8 /// double homeobox 4 like 4 /// LOC100288289 /// double homeobox 4 like 5 /// LOC100291626 /// double homeobox 4 like 6 /// LOC652301 double homeobox 4 like 7 /// double homeobox 4 like 8 /// double homeobox protein 4-like protein 2-like /// double homeobox protein 4-like /// double homeobox protein 4-like protein 4-like 218355_at SKY92 KIF4A kinesin family member 4A 218662_s_at SKY92 NCAPG non-SMC condensin I complex, subunit G 220351_at SKY92 ACKR4 atypical chemokine receptor 4 221677_s_at SKY92 DONSON downstream neighbor of SON 221826_at SKY92 ANGEL2 angel homolog 2 (Drosophila) 223313_s_at Virtual MAGED4 /// melanoma antigen family D, 4 /// t(4; 14) MAGED4B /// melanoma antigen family D, 4B /// SNORA11D/// small nucleolar RNA, H/ACA box SNORA11E 11D /// small nucleolar RNA, H/ACA box 11E 223381_at SKY92 NUF2 NUF2, NDC80 kinetochore complex component 223811_s_at SKY92 GET4 /// SUN1 golgi to ER traffic protein 4 homolog (S. cerevisiae) /// Sad1 and UNC84 domain containing 1 228416_at SKY92 ACVR2A activin A receptor, type IIA 238780_s_at SKY92 KCNJ5 potassium inwardly-rectifying channel, subfamily J, member 5 242180_at SKY92 TSPAN16 tetraspanin 16

TABLE 13 Markers present in both Table 1 and Table 11. Exemplary beta values (i.e., weights) and thresholds are provided for each probeset. The thresholds were determined such that each individual probeset classifies an individual as disclosed herein. Probesets Beta Gene Symbol Threshold 200701_at −0.0210 NPC2 0.0190 200775_s_at 0.0163 HNRNPK /// MIR7-1 0.0152 200875_s_at 0.0437 MIR1292 /// 0.0385 NOP56 /// SNORD110 /// SNORD57 /// SNORD86 200933_x_at −0.0323 RPS4X 0.0245 201102_s_at 0.0349 PFKL 0.0449 201292_at −0.0372 TOP2A 0.0310 201307_at 0.0165 SEPT11 0.0181 201398_s_at −0.0254 TRAM1 0.0263 201555_at −0.0052 MCM3 0.0037 201795_at 0.0067 LBR 0.0069 201930_at −0.0090 MCM6 0.0091 202107_s_at 0.0225 MCM2 0.0266 202322_s_at 0.0129 GGPS1 0.0153 202532_s_at −0.0006 DHFR 0.0005 202542_s_at 0.0870 AIMP1 0.0945 202553_s_at 0.0054 SYF2 0.0054 202728_s_at −0.1105 LTBP1 0.0998 202813_at 0.0548 TARBP1 0.0472 202842_s_at −0.0626 DNAJB9 0.0776 202884_s_at 0.0714 PPP2R1B 0.0544 203145_at −0.0002 SPAG5 0.0002 204026_s_at 0.0046 ZWINT 0.0049 204379_s_at 0.0594 FGFR3 0.0052 205046_at 0.0087 CENPE 0.0105 206204_at 0.0477 GRB14 0.0606 207618_s_at 0.0746 BCS1L 0.0660 208232_x_at −0.0493 NRG1 0.0801 208667_s_at −0.0390 ST13 0.0395 208732_at −0.0618 RAB2A 0.0698 208747_s_at −0.0874 C1S 0.0882 208904_s_at −0.0334 RPS28 0.0247 208942_s_at −0.0997 SEC62 0.0935 208967_s_at 0.0113 AK2 0.0087 209026_x_at 0.0255 TUBB 0.0316 209683_at −0.0561 FAM49A 0.0293 210334_x_at 0.0175 BIRC5 0.0193 211714_x_at 0.0221 TUBB 0.0287 211963_s_at 0.0303 ARPC5 0.0334 212055_at 0.0384 TPGS2 0.0352 212282_at 0.0530 TMEM97 0.0515 212788_x_at −0.0164 FTL 0.0131 213002_at −0.0418 MARCKS 0.0385 213007_at −0.0106 FANCI 0.0099 213350_at 0.0056 RPS11 0.0087 214150_x_at −0.0349 ATP6V0E1 0.0243 214482_at 0.0861 ZBTB25 0.0834 214612_x_at 0.0496 MAGEA6 0.0611 215177_s_at −0.0768 ITGA6 0.0835 215181_at −0.0342 CDH22 0.0380 216473_x_at −0.0576 DUX2 /// DUX4 /// DUX4L2 /// 0.0664 DUX4L3 /// DUX4L4 /// DUX4L5 /// DUX4L6 /// DUX4L7 /// LOC100288627/// LOC100288657 /// LOC652119 217548_at −0.0423 LOC100129502 0.0460 217728_at 0.0773 S100A6 0.0740 217732_s_at −0.0252 ITM2B 0.0297 217824_at −0.0041 UBE2J1 0.0035 217852_s_at 0.0008 ARL8B 0.0007 218355_at 0.0116 KIF4A 0.0126 218365_s_at 0.0035 DARS2 0.0028 218662_s_at −0.0176 NCAPG 0.0213 219510_at −0.0097 POLQ 0.0093 219550_at 0.0559 ROBO3 0.0522 220351_at 0.0420 CCRL1 0.0383 221041_s_at −0.0520 SLC17A5 0.0369 221606_s_at 0.0208 HMGN5 0.0163 221677_s_at 0.0126 DONSON 0.0146 221755_at 0.0396 EHBP1L1 0.0317 221826_at 0.0200 ANGEL2 0.0147 222154_s_at 0.0154 SPATS2L 0.0148 222680_s_at 0.0205 DTL 0.0213 222713_s_at 0.0278 FANCF 0.0239 223381_at −0.0070 NUF2 0.0106 223811_s_at 0.0556 GET4 /// SUN1 0.0562 224009_x_at −0.0520 DHRS9 0.0583 225366_at 0.0140 PGM2 0.0139 225601_at 0.0750 HMGB3 0.0659 226217_at −0.0319 SLC30A7 0.0229 226218_at −0.0644 IL7R 0.0675 226742_at −0.0345 SAR1B 0.0312 228416_at −0.0778 ACVR2A 0.1187 230034_x_at −0.0330 MRPL41 0.0257 231210_at 0.0093 C11orf85 0.0093 231738_at 0.0686 PCDHB7 0.0714 231989_s_at 0.0730 61E3.4 /// LOC100132247 /// 0.0681 LOC100271836 /// LOC100652992 /// LOC613037 /// LOC728888 /// NPIPL3 /// SLC7A5P1 ///SMG1P1 233399_x_at −0.0184 TMED10P1 ///ZNF252 0.0182 233437_at 0.0446 GABRA4 0.0493 238116_at 0.0661 DYNLRB2 0.0811 238662_at 0.0490 ATPBD4 0.0452 238780_s_at −0.0529 — 0.0551 239054_at −0.1088 SFMBT1 0.0904 242180_at −0.0585 TSPAN16 0.0546 243018_at 0.0407 — 0.0484 38158_at 0.0423 ESPL1 0.0424 AFFX- 0.0525 STAT1 /// STAT1 0.0354 HUMISGF3A/M97935_MA_at

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1-17. (canceled)
 18. A method for classifying an individual with multiple myeloma based on the likelihood of response to treatment with an immunomodulatory drug (IMiD), the method comprising gene expression profiling, wherein said individual is classified as i) a likely responder to thalidomide or an analog thereof which is not substituted with NH2 or CH3 at the C4 of the phthaloyl ring and a likely responder to a thalidomide analog which is substituted with NH2 or CH3 at the C4 of the phthaloyl ring, ii) a likely responder to a thalidomide analog which is substituted with NH2 or CH3 at the C4 of the phthaloyl ring and a likely non-responder to thalidomide or an analog thereof which is not substituted with NH2 or CH3 at the C4 of the phthaloyl ring, or iii) a likely non-responder to a thalidomide analog which is substituted with NH2 or CH3 at the C4 of the phthaloyl ring and a likely responder to thalidomide or an analog thereof which is not substituted with NH2 or CH3 at the C4 of the phthaloyl ring; the method comprising: a) determining in a sample from said individual the level of expression of each marker listed in Table 1; b) determining in a sample from said individual the level of expression of at least one marker selected from Table 11; c) determining in a sample from said individual the presence of the t(4;14) translocation using gene expression profiling; d) determining in a sample from said individual the level of expression of at least one marker in Table 3; and/or e) determining in a sample from said individual the presence of the t(11;14) translocation using gene expression profiling; wherein the individual is classified based on at least one of steps a), b), c), d) and e).
 19. The method of claim 18, wherein the method comprises a) determining in a sample from said individual the level of expression of at least one markers selected from Table 11 and/or b) determining in a sample from said individual the presence of the t(4;14) translocation; and c) determining in a sample from said individual the level of expression of at least one marker in Table 3 and/or d) determining in a sample from said individual the presence of the t(11;14) translocation; wherein the individual is classified based on steps a) and/or b) and on steps c) and/or d).
 20. The method of claim 18 comprising determining the level of expression of the markers from Table 2, and/or the markers from Table
 4. 21. The method of claim 18, wherein the level of marker expression is determined by detection of RNA.
 22. The method of claim 18, wherein the thalidomide analog which is substituted with NH₂ or CH₃ at the C4 of the phthaloyl ring is lenalidomide or pomalidomide.
 23. The method of claim 18, wherein the sample comprises plasma cells.
 24. A method for treating an individual for multiple myeloma comprising a) determining in a sample from said individual the level of expression of each marker listed in Table 1; b) determining in a sample from said individual the level of expression of at least one marker selected from Table 11; c) determining in a sample from said individual the presence of the t(4;14) translocation using gene expression profiling; d) determining in a sample from said individual the level of expression of at least one marker in Table 3; and/or e) determining in a sample from said individual the presence of the t(11;14) translocation using gene expression profiling; determining based on steps a), b), c), d) and/or e) a treatment of the individual, and treating said individual accordingly.
 25. The method of claim 24, wherein the method comprises a) determining in a sample from said individual the level of expression of at least one markers selected from Table 11 and/or b) determining in a sample from said individual the presence of the t(4;14) translocation; and c) determining in a sample from said individual the level of expression of at least one marker in Table 3 and/or d) determining in a sample from said individual the presence of the t(11;14) translocation; wherein the individual is classified based on steps a) and/or b) and on steps c) and/or d).
 26. The method of claim 24 comprising determining the level of expression of the markers from Table 2, and/or the markers from Table
 4. 27. The method of claim 24, wherein the level of marker expression is determined by detection of RNA.
 28. The method of claim 24, wherein the thalidomide analog which is substituted with NH₂ or CH₃ at the C4 of the phthaloyl ring is lenalidomide or pomalidomide.
 29. The method of claim 24 wherein the sample comprises plasma cells.
 30. The method of claim 24, wherein said individual is treated with thalidomide or an analog thereof which is not substituted with NH₂ or CH₃ at the C4 of the phthaloyl ring.
 31. The method of claim 24, wherein said individual is treated with a thalidomide analog which is substituted with NH₂ or CH₃ at the C4 of the phthaloyl ring.
 32. A method for classifying an individual with multiple myeloma based on the likelihood of response to treatment with an immunomodulatory drug (IMiD), wherein said individual is classified as i) a likely responder to thalidomide or an analog thereof which is not substituted with NH₂ or CH₃ at the C4 of the phthaloyl ring and a likely responder to a thalidomide analog which is substituted with NH₂ or CH₃ at the C4 of the phthaloyl ring, ii) a likely responder to a thalidomide analog which is substituted with NH₂ or CH₃ at the C4 of the phthaloyl ring and a likely non-responder to thalidomide or an analog thereof which is not substituted with NH₂ or CH₃ at the C4 of the phthaloyl ring, or iii) a likely non-responder to a thalidomide analog which is substituted with NH₂ or CH₃ at the C4 of the phthaloyl ring and a likely responder to thalidomide or an analog thereof which is not substituted with NH₂ or CH₃ at the C4 of the phthaloyl ring; the method comprising: a) determining in a sample from said individual the level of expression of at least one marker selected from Table 11; b) determining in a sample from said individual the presence of the t(4;14) translocation; c) determining in a sample from said individual the level of expression of at least one marker in Table 3; and/or d) determining in a sample from said individual the presence of the t(11;14) translocation; wherein the individual is classified based on at least one of steps a), b), c), and d).
 33. The method of claim 32, wherein the presence of the t(4;14) translocation and/or the t(11;14) translocation is determined using fluorescence in situ hybridization (FISH). 